Overview

Brought to you by YData

Dataset statistics

Number of variables23
Number of observations3678
Missing cells6711
Missing cells (%)7.9%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.2 MiB
Average record size in memory632.1 B

Variable types

Categorical10
Text3
Numeric10

Alerts

area is highly overall correlated with bathroom and 5 other fieldsHigh correlation
bathroom is highly overall correlated with area and 5 other fieldsHigh correlation
bedRoom is highly overall correlated with area and 5 other fieldsHigh correlation
built_up_area is highly overall correlated with area and 4 other fieldsHigh correlation
carpet_area is highly overall correlated with area and 5 other fieldsHigh correlation
facing is highly overall correlated with built_up_areaHigh correlation
price is highly overall correlated with area and 7 other fieldsHigh correlation
price_per_sqft is highly overall correlated with priceHigh correlation
property_type is highly overall correlated with bedRoom and 2 other fieldsHigh correlation
servant room is highly overall correlated with bathroom and 1 other fieldsHigh correlation
super_built_up_area is highly overall correlated with area and 7 other fieldsHigh correlation
store room is highly imbalanced (55.7%)Imbalance
facing has 1045 (28.4%) missing valuesMissing
super_built_up_area has 1803 (49.0%) missing valuesMissing
built_up_area has 1987 (54.0%) missing valuesMissing
carpet_area has 1805 (49.1%) missing valuesMissing
area is highly skewed (γ1 = 29.73501717)Skewed
built_up_area is highly skewed (γ1 = 40.71858453)Skewed
carpet_area is highly skewed (γ1 = 24.33974292)Skewed
floorNum has 129 (3.5%) zerosZeros
luxury_score has 462 (12.6%) zerosZeros

Reproduction

Analysis started2024-08-26 10:27:22.347460
Analysis finished2024-08-26 10:27:53.925583
Duration31.58 seconds
Software versionydata-profiling vv4.9.0
Download configurationconfig.json

Variables

property_type
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size248.7 KiB
flat
2819 
house
859 

Length

Max length5
Median length4
Mean length4.2335508
Min length4

Characters and Unicode

Total characters15571
Distinct characters9
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowflat
2nd rowflat
3rd rowflat
4th rowflat
5th rowflat

Common Values

ValueCountFrequency (%)
flat 2819
76.6%
house 859
 
23.4%

Length

2024-08-26T15:57:54.159305image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-08-26T15:57:54.412564image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
flat 2819
76.6%
house 859
 
23.4%

Most occurring characters

ValueCountFrequency (%)
f 2819
18.1%
l 2819
18.1%
a 2819
18.1%
t 2819
18.1%
h 859
 
5.5%
o 859
 
5.5%
u 859
 
5.5%
s 859
 
5.5%
e 859
 
5.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 15571
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
f 2819
18.1%
l 2819
18.1%
a 2819
18.1%
t 2819
18.1%
h 859
 
5.5%
o 859
 
5.5%
u 859
 
5.5%
s 859
 
5.5%
e 859
 
5.5%

Most occurring scripts

ValueCountFrequency (%)
Latin 15571
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
f 2819
18.1%
l 2819
18.1%
a 2819
18.1%
t 2819
18.1%
h 859
 
5.5%
o 859
 
5.5%
u 859
 
5.5%
s 859
 
5.5%
e 859
 
5.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 15571
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
f 2819
18.1%
l 2819
18.1%
a 2819
18.1%
t 2819
18.1%
h 859
 
5.5%
o 859
 
5.5%
u 859
 
5.5%
s 859
 
5.5%
e 859
 
5.5%
Distinct676
Distinct (%)18.4%
Missing1
Missing (%)< 0.1%
Memory size294.0 KiB
2024-08-26T15:57:55.240931image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length49
Median length39
Mean length16.869459
Min length1

Characters and Unicode

Total characters62029
Distinct characters41
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique308 ?
Unique (%)8.4%

Sample

1st rowvatika gurgaon
2nd rowparas dews
3rd rowshapoorji pallonji joyville gurugram
4th rowtulip violet
5th rowsatya the hermitage
ValueCountFrequency (%)
independent 491
 
5.1%
the 350
 
3.6%
dlf 220
 
2.3%
park 209
 
2.2%
city 166
 
1.7%
emaar 155
 
1.6%
global 153
 
1.6%
m3m 152
 
1.6%
signature 150
 
1.5%
heights 134
 
1.4%
Other values (783) 7500
77.5%
2024-08-26T15:57:56.495356image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 6711
 
10.8%
6005
 
9.7%
a 5862
 
9.5%
r 4173
 
6.7%
n 4163
 
6.7%
i 3830
 
6.2%
t 3720
 
6.0%
s 3474
 
5.6%
l 2944
 
4.7%
o 2756
 
4.4%
Other values (31) 18391
29.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 55479
89.4%
Space Separator 6005
 
9.7%
Decimal Number 527
 
0.8%
Other Punctuation 10
 
< 0.1%
Dash Punctuation 8
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 6711
12.1%
a 5862
 
10.6%
r 4173
 
7.5%
n 4163
 
7.5%
i 3830
 
6.9%
t 3720
 
6.7%
s 3474
 
6.3%
l 2944
 
5.3%
o 2756
 
5.0%
d 2489
 
4.5%
Other values (16) 15357
27.7%
Decimal Number
ValueCountFrequency (%)
3 207
39.3%
2 82
 
15.6%
1 75
 
14.2%
6 56
 
10.6%
8 32
 
6.1%
4 19
 
3.6%
5 17
 
3.2%
0 13
 
2.5%
9 13
 
2.5%
7 13
 
2.5%
Other Punctuation
ValueCountFrequency (%)
, 7
70.0%
/ 2
 
20.0%
. 1
 
10.0%
Space Separator
ValueCountFrequency (%)
6005
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 8
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 55479
89.4%
Common 6550
 
10.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 6711
12.1%
a 5862
 
10.6%
r 4173
 
7.5%
n 4163
 
7.5%
i 3830
 
6.9%
t 3720
 
6.7%
s 3474
 
6.3%
l 2944
 
5.3%
o 2756
 
5.0%
d 2489
 
4.5%
Other values (16) 15357
27.7%
Common
ValueCountFrequency (%)
6005
91.7%
3 207
 
3.2%
2 82
 
1.3%
1 75
 
1.1%
6 56
 
0.9%
8 32
 
0.5%
4 19
 
0.3%
5 17
 
0.3%
0 13
 
0.2%
9 13
 
0.2%
Other values (5) 31
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 62029
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 6711
 
10.8%
6005
 
9.7%
a 5862
 
9.5%
r 4173
 
6.7%
n 4163
 
6.7%
i 3830
 
6.2%
t 3720
 
6.0%
s 3474
 
5.6%
l 2944
 
4.7%
o 2756
 
4.4%
Other values (31) 18391
29.6%

sector
Text

Distinct115
Distinct (%)3.1%
Missing0
Missing (%)0.0%
Memory size266.9 KiB
2024-08-26T15:57:57.188321image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length26
Median length9
Mean length9.3189233
Min length3

Characters and Unicode

Total characters34275
Distinct characters31
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowsector 83
2nd rowsector 106
3rd rowsector 102
4th rowsector 69
5th rowsector 103
ValueCountFrequency (%)
sector 3450
46.7%
road 178
 
2.4%
sohna 166
 
2.2%
85 108
 
1.5%
102 107
 
1.4%
92 100
 
1.4%
69 93
 
1.3%
90 89
 
1.2%
65 87
 
1.2%
81 87
 
1.2%
Other values (107) 2921
39.5%
2024-08-26T15:57:58.250564image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
o 3805
11.1%
3708
10.8%
s 3694
10.8%
r 3694
10.8%
e 3548
10.4%
c 3501
10.2%
t 3461
10.1%
1 1076
 
3.1%
0 804
 
2.3%
8 776
 
2.3%
Other values (21) 6208
18.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 23307
68.0%
Decimal Number 7260
 
21.2%
Space Separator 3708
 
10.8%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 3805
16.3%
s 3694
15.8%
r 3694
15.8%
e 3548
15.2%
c 3501
15.0%
t 3461
14.8%
a 697
 
3.0%
d 249
 
1.1%
n 229
 
1.0%
h 203
 
0.9%
Other values (10) 226
 
1.0%
Decimal Number
ValueCountFrequency (%)
1 1076
14.8%
0 804
11.1%
8 776
10.7%
9 763
10.5%
6 741
10.2%
7 681
9.4%
2 679
9.4%
3 664
9.1%
5 593
8.2%
4 483
6.7%
Space Separator
ValueCountFrequency (%)
3708
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 23307
68.0%
Common 10968
32.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
o 3805
16.3%
s 3694
15.8%
r 3694
15.8%
e 3548
15.2%
c 3501
15.0%
t 3461
14.8%
a 697
 
3.0%
d 249
 
1.1%
n 229
 
1.0%
h 203
 
0.9%
Other values (10) 226
 
1.0%
Common
ValueCountFrequency (%)
3708
33.8%
1 1076
 
9.8%
0 804
 
7.3%
8 776
 
7.1%
9 763
 
7.0%
6 741
 
6.8%
7 681
 
6.2%
2 679
 
6.2%
3 664
 
6.1%
5 593
 
5.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 34275
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o 3805
11.1%
3708
10.8%
s 3694
10.8%
r 3694
10.8%
e 3548
10.4%
c 3501
10.2%
t 3461
10.1%
1 1076
 
3.1%
0 804
 
2.3%
8 776
 
2.3%
Other values (21) 6208
18.1%

price
Real number (ℝ)

HIGH CORRELATION 

Distinct473
Distinct (%)12.9%
Missing17
Missing (%)0.5%
Infinite0
Infinite (%)0.0%
Mean2.5332996
Minimum0.07
Maximum31.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size57.5 KiB
2024-08-26T15:57:58.731689image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.07
5-th percentile0.37
Q10.95
median1.52
Q32.75
95-th percentile8.5
Maximum31.5
Range31.43
Interquartile range (IQR)1.8

Descriptive statistics

Standard deviation2.9802978
Coefficient of variation (CV)1.176449
Kurtosis14.937921
Mean2.5332996
Median Absolute Deviation (MAD)0.72
Skewness3.2796914
Sum9274.41
Variance8.8821749
MonotonicityNot monotonic
2024-08-26T15:57:59.482276image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.25 80
 
2.2%
1.2 65
 
1.8%
1.5 64
 
1.7%
0.9 63
 
1.7%
1.1 62
 
1.7%
1.4 60
 
1.6%
1.3 57
 
1.5%
0.95 52
 
1.4%
2 52
 
1.4%
1.6 48
 
1.3%
Other values (463) 3058
83.1%
ValueCountFrequency (%)
0.07 1
 
< 0.1%
0.16 1
 
< 0.1%
0.17 1
 
< 0.1%
0.19 1
 
< 0.1%
0.2 8
0.2%
0.21 6
0.2%
0.22 8
0.2%
0.23 1
 
< 0.1%
0.24 6
0.2%
0.25 11
0.3%
ValueCountFrequency (%)
31.5 1
 
< 0.1%
27.5 1
 
< 0.1%
26 2
0.1%
25 1
 
< 0.1%
24 1
 
< 0.1%
23 1
 
< 0.1%
22 1
 
< 0.1%
20 3
0.1%
19.5 2
0.1%
19 3
0.1%

price_per_sqft
Real number (ℝ)

HIGH CORRELATION 

Distinct2651
Distinct (%)72.4%
Missing17
Missing (%)0.5%
Infinite0
Infinite (%)0.0%
Mean13891.177
Minimum4
Maximum600000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size57.5 KiB
2024-08-26T15:57:59.966349image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum4
5-th percentile4716
Q16818
median9020
Q313878
95-th percentile33333
Maximum600000
Range599996
Interquartile range (IQR)7060

Descriptive statistics

Standard deviation23207.072
Coefficient of variation (CV)1.6706339
Kurtosis186.97704
Mean13891.177
Median Absolute Deviation (MAD)2793
Skewness11.438681
Sum50855599
Variance5.3856817 × 108
MonotonicityNot monotonic
2024-08-26T15:58:00.476362image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10000 27
 
0.7%
8000 19
 
0.5%
5000 17
 
0.5%
12500 14
 
0.4%
22222 13
 
0.4%
6666 13
 
0.4%
11111 13
 
0.4%
8333 12
 
0.3%
7500 12
 
0.3%
6000 11
 
0.3%
Other values (2641) 3510
95.4%
(Missing) 17
 
0.5%
ValueCountFrequency (%)
4 1
< 0.1%
5 1
< 0.1%
7 1
< 0.1%
9 1
< 0.1%
53 1
< 0.1%
57 1
< 0.1%
58 2
0.1%
60 1
< 0.1%
61 1
< 0.1%
79 1
< 0.1%
ValueCountFrequency (%)
600000 1
< 0.1%
400000 1
< 0.1%
315789 1
< 0.1%
308333 1
< 0.1%
290948 1
< 0.1%
283333 1
< 0.1%
266666 1
< 0.1%
261194 1
< 0.1%
245398 1
< 0.1%
241666 1
< 0.1%

area
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct1312
Distinct (%)35.8%
Missing17
Missing (%)0.5%
Infinite0
Infinite (%)0.0%
Mean2887.9309
Minimum50
Maximum875000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size57.5 KiB
2024-08-26T15:58:00.879573image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum50
5-th percentile519
Q11233
median1733
Q32300
95-th percentile4246
Maximum875000
Range874950
Interquartile range (IQR)1067

Descriptive statistics

Standard deviation23164.353
Coefficient of variation (CV)8.02109
Kurtosis942.28687
Mean2887.9309
Median Absolute Deviation (MAD)533
Skewness29.735017
Sum10572715
Variance5.3658727 × 108
MonotonicityNot monotonic
2024-08-26T15:58:01.204399image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1650 54
 
1.5%
1350 48
 
1.3%
1800 47
 
1.3%
1950 43
 
1.2%
3240 43
 
1.2%
2700 39
 
1.1%
900 38
 
1.0%
2000 33
 
0.9%
2250 25
 
0.7%
2400 23
 
0.6%
Other values (1302) 3268
88.9%
ValueCountFrequency (%)
50 4
0.1%
55 1
 
< 0.1%
56 1
 
< 0.1%
57 1
 
< 0.1%
60 2
0.1%
61 1
 
< 0.1%
67 2
0.1%
70 1
 
< 0.1%
72 1
 
< 0.1%
76 1
 
< 0.1%
ValueCountFrequency (%)
875000 1
< 0.1%
642857 1
< 0.1%
620000 1
< 0.1%
566667 1
< 0.1%
215517 1
< 0.1%
98978 1
< 0.1%
82781 1
< 0.1%
65517 2
0.1%
65261 1
< 0.1%
58228 1
< 0.1%
Distinct2355
Distinct (%)64.0%
Missing0
Missing (%)0.0%
Memory size428.3 KiB
2024-08-26T15:58:02.105443image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length124
Median length119
Mean length54.240348
Min length12

Characters and Unicode

Total characters199496
Distinct characters35
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1848 ?
Unique (%)50.2%

Sample

1st rowSuper Built up area 1980(183.95 sq.m.)
2nd rowSuper Built up area 1760(163.51 sq.m.)Built Up area: 1186 sq.ft. (110.18 sq.m.)Carpet area: 1130 sq.ft. (104.98 sq.m.)
3rd rowSuper Built up area 2162(200.86 sq.m.)
4th rowBuilt Up area: 1578 (146.6 sq.m.)
5th rowSuper Built up area 1450(134.71 sq.m.)Carpet area: 1081 sq.ft. (100.43 sq.m.)
ValueCountFrequency (%)
area 5575
18.5%
sq.m 3656
12.1%
up 3021
 
10.0%
built 2317
 
7.7%
super 1875
 
6.2%
sq.ft 1752
 
5.8%
sq.m.)carpet 1186
 
3.9%
sq.m.)built 702
 
2.3%
carpet 683
 
2.3%
plot 681
 
2.3%
Other values (2846) 8704
28.9%
2024-08-26T15:58:03.404877image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
26474
 
13.3%
. 20397
 
10.2%
a 13159
 
6.6%
r 9459
 
4.7%
e 9323
 
4.7%
1 9207
 
4.6%
s 7570
 
3.8%
q 7434
 
3.7%
t 7327
 
3.7%
u 6771
 
3.4%
Other values (25) 82375
41.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 82786
41.5%
Decimal Number 47150
23.6%
Space Separator 26474
 
13.3%
Other Punctuation 23416
 
11.7%
Uppercase Letter 8596
 
4.3%
Close Punctuation 5537
 
2.8%
Open Punctuation 5537
 
2.8%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 13159
15.9%
r 9459
11.4%
e 9323
11.3%
s 7570
9.1%
q 7434
9.0%
t 7327
8.9%
u 6771
8.2%
p 6769
8.2%
m 5546
6.7%
l 3702
 
4.5%
Other values (5) 5726
6.9%
Decimal Number
ValueCountFrequency (%)
1 9207
19.5%
0 6629
14.1%
2 5692
12.1%
5 4715
10.0%
3 3962
8.4%
4 3713
7.9%
6 3674
 
7.8%
7 3255
 
6.9%
8 3158
 
6.7%
9 3145
 
6.7%
Uppercase Letter
ValueCountFrequency (%)
B 3021
35.1%
S 1875
21.8%
C 1873
21.8%
U 1146
 
13.3%
P 681
 
7.9%
Other Punctuation
ValueCountFrequency (%)
. 20397
87.1%
: 3019
 
12.9%
Space Separator
ValueCountFrequency (%)
26474
100.0%
Close Punctuation
ValueCountFrequency (%)
) 5537
100.0%
Open Punctuation
ValueCountFrequency (%)
( 5537
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 108114
54.2%
Latin 91382
45.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 13159
14.4%
r 9459
10.4%
e 9323
10.2%
s 7570
8.3%
q 7434
8.1%
t 7327
8.0%
u 6771
7.4%
p 6769
7.4%
m 5546
 
6.1%
l 3702
 
4.1%
Other values (10) 14322
15.7%
Common
ValueCountFrequency (%)
26474
24.5%
. 20397
18.9%
1 9207
 
8.5%
0 6629
 
6.1%
2 5692
 
5.3%
) 5537
 
5.1%
( 5537
 
5.1%
5 4715
 
4.4%
3 3962
 
3.7%
4 3713
 
3.4%
Other values (5) 16251
15.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 199496
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
26474
 
13.3%
. 20397
 
10.2%
a 13159
 
6.6%
r 9459
 
4.7%
e 9323
 
4.7%
1 9207
 
4.6%
s 7570
 
3.8%
q 7434
 
3.7%
t 7327
 
3.7%
u 6771
 
3.4%
Other values (25) 82375
41.3%

bedRoom
Real number (ℝ)

HIGH CORRELATION 

Distinct19
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.3599782
Minimum1
Maximum21
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size57.5 KiB
2024-08-26T15:58:03.705708image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q12
median3
Q34
95-th percentile6
Maximum21
Range20
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.8973801
Coefficient of variation (CV)0.56470012
Kurtosis18.218941
Mean3.3599782
Median Absolute Deviation (MAD)1
Skewness3.4857171
Sum12358
Variance3.6000513
MonotonicityNot monotonic
2024-08-26T15:58:04.000550image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
3 1497
40.7%
2 942
25.6%
4 660
17.9%
5 210
 
5.7%
1 124
 
3.4%
6 74
 
2.0%
9 41
 
1.1%
8 30
 
0.8%
7 28
 
0.8%
12 28
 
0.8%
Other values (9) 44
 
1.2%
ValueCountFrequency (%)
1 124
 
3.4%
2 942
25.6%
3 1497
40.7%
4 660
17.9%
5 210
 
5.7%
6 74
 
2.0%
7 28
 
0.8%
8 30
 
0.8%
9 41
 
1.1%
10 20
 
0.5%
ValueCountFrequency (%)
21 1
 
< 0.1%
20 1
 
< 0.1%
19 2
 
0.1%
18 2
 
0.1%
16 12
0.3%
14 1
 
< 0.1%
13 4
 
0.1%
12 28
0.8%
11 1
 
< 0.1%
10 20
0.5%

bathroom
Real number (ℝ)

HIGH CORRELATION 

Distinct19
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.4244154
Minimum1
Maximum21
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size57.5 KiB
2024-08-26T15:58:04.290394image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q12
median3
Q34
95-th percentile6
Maximum21
Range20
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.9478158
Coefficient of variation (CV)0.56880241
Kurtosis17.548118
Mean3.4244154
Median Absolute Deviation (MAD)1
Skewness3.2493833
Sum12595
Variance3.7939863
MonotonicityNot monotonic
2024-08-26T15:58:04.589337image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
3 1078
29.3%
2 1047
28.5%
4 820
22.3%
5 294
 
8.0%
1 156
 
4.2%
6 117
 
3.2%
9 41
 
1.1%
7 40
 
1.1%
8 25
 
0.7%
12 22
 
0.6%
Other values (9) 38
 
1.0%
ValueCountFrequency (%)
1 156
 
4.2%
2 1047
28.5%
3 1078
29.3%
4 820
22.3%
5 294
 
8.0%
6 117
 
3.2%
7 40
 
1.1%
8 25
 
0.7%
9 41
 
1.1%
10 9
 
0.2%
ValueCountFrequency (%)
21 1
 
< 0.1%
20 3
 
0.1%
18 4
 
0.1%
17 3
 
0.1%
16 8
 
0.2%
14 2
 
0.1%
13 4
 
0.1%
12 22
0.6%
11 4
 
0.1%
10 9
0.2%

balcony
Categorical

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size238.2 KiB
3+
1172 
3
1074 
2
885 
1
365 
0
182 

Length

Max length2
Median length1
Mean length1.3186514
Min length1

Characters and Unicode

Total characters4850
Distinct characters5
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row3+
3rd row3+
4th row2
5th row3

Common Values

ValueCountFrequency (%)
3+ 1172
31.9%
3 1074
29.2%
2 885
24.1%
1 365
 
9.9%
0 182
 
4.9%

Length

2024-08-26T15:58:04.888797image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-08-26T15:58:05.148658image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
3 2246
61.1%
2 885
 
24.1%
1 365
 
9.9%
0 182
 
4.9%

Most occurring characters

ValueCountFrequency (%)
3 2246
46.3%
+ 1172
24.2%
2 885
 
18.2%
1 365
 
7.5%
0 182
 
3.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 3678
75.8%
Math Symbol 1172
 
24.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
3 2246
61.1%
2 885
 
24.1%
1 365
 
9.9%
0 182
 
4.9%
Math Symbol
ValueCountFrequency (%)
+ 1172
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 4850
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
3 2246
46.3%
+ 1172
24.2%
2 885
 
18.2%
1 365
 
7.5%
0 182
 
3.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4850
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3 2246
46.3%
+ 1172
24.2%
2 885
 
18.2%
1 365
 
7.5%
0 182
 
3.8%

floorNum
Real number (ℝ)

ZEROS 

Distinct43
Distinct (%)1.2%
Missing19
Missing (%)0.5%
Infinite0
Infinite (%)0.0%
Mean6.7969391
Minimum0
Maximum51
Zeros129
Zeros (%)3.5%
Negative0
Negative (%)0.0%
Memory size57.5 KiB
2024-08-26T15:58:05.457325image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median5
Q310
95-th percentile18
Maximum51
Range51
Interquartile range (IQR)8

Descriptive statistics

Standard deviation6.0121557
Coefficient of variation (CV)0.8845387
Kurtosis4.5164183
Mean6.7969391
Median Absolute Deviation (MAD)3
Skewness1.6940035
Sum24870
Variance36.146016
MonotonicityNot monotonic
2024-08-26T15:58:05.780430image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=43)
ValueCountFrequency (%)
3 498
13.5%
2 494
13.4%
1 351
 
9.5%
4 316
 
8.6%
8 195
 
5.3%
6 183
 
5.0%
10 179
 
4.9%
7 176
 
4.8%
5 169
 
4.6%
9 161
 
4.4%
Other values (33) 937
25.5%
ValueCountFrequency (%)
0 129
 
3.5%
1 351
9.5%
2 494
13.4%
3 498
13.5%
4 316
8.6%
5 169
 
4.6%
6 183
 
5.0%
7 176
 
4.8%
8 195
 
5.3%
9 161
 
4.4%
ValueCountFrequency (%)
51 1
 
< 0.1%
45 1
 
< 0.1%
44 1
 
< 0.1%
43 2
0.1%
40 1
 
< 0.1%
39 2
0.1%
38 1
 
< 0.1%
35 2
0.1%
34 2
0.1%
33 4
0.1%

facing
Categorical

HIGH CORRELATION  MISSING 

Distinct8
Distinct (%)0.3%
Missing1045
Missing (%)28.4%
Memory size258.2 KiB
East
624 
North-East
623 
North
387 
West
249 
South
231 
Other values (3)
519 

Length

Max length10
Median length5
Mean length6.837068
Min length4

Characters and Unicode

Total characters18002
Distinct characters13
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowWest
2nd rowWest
3rd rowNorth-East
4th rowNorth
5th rowSouth

Common Values

ValueCountFrequency (%)
East 624
17.0%
North-East 623
16.9%
North 387
 
10.5%
West 249
 
6.8%
South 231
 
6.3%
North-West 193
 
5.2%
South-East 173
 
4.7%
South-West 153
 
4.2%
(Missing) 1045
28.4%

Length

2024-08-26T15:58:06.090268image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-08-26T15:58:06.370116image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
east 624
23.7%
north-east 623
23.7%
north 387
14.7%
west 249
 
9.5%
south 231
 
8.8%
north-west 193
 
7.3%
south-east 173
 
6.6%
south-west 153
 
5.8%

Most occurring characters

ValueCountFrequency (%)
t 3775
21.0%
s 2015
11.2%
o 1760
9.8%
h 1760
9.8%
E 1420
 
7.9%
a 1420
 
7.9%
N 1203
 
6.7%
r 1203
 
6.7%
- 1142
 
6.3%
W 595
 
3.3%
Other values (3) 1709
9.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 13085
72.7%
Uppercase Letter 3775
 
21.0%
Dash Punctuation 1142
 
6.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
t 3775
28.8%
s 2015
15.4%
o 1760
13.5%
h 1760
13.5%
a 1420
 
10.9%
r 1203
 
9.2%
e 595
 
4.5%
u 557
 
4.3%
Uppercase Letter
ValueCountFrequency (%)
E 1420
37.6%
N 1203
31.9%
W 595
15.8%
S 557
 
14.8%
Dash Punctuation
ValueCountFrequency (%)
- 1142
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 16860
93.7%
Common 1142
 
6.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
t 3775
22.4%
s 2015
12.0%
o 1760
10.4%
h 1760
10.4%
E 1420
 
8.4%
a 1420
 
8.4%
N 1203
 
7.1%
r 1203
 
7.1%
W 595
 
3.5%
e 595
 
3.5%
Other values (2) 1114
 
6.6%
Common
ValueCountFrequency (%)
- 1142
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 18002
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
t 3775
21.0%
s 2015
11.2%
o 1760
9.8%
h 1760
9.8%
E 1420
 
7.9%
a 1420
 
7.9%
N 1203
 
6.7%
r 1203
 
6.7%
- 1142
 
6.3%
W 595
 
3.3%
Other values (3) 1709
9.5%

agePossession
Categorical

Distinct6
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size281.5 KiB
Relatively New
1646 
New Property
593 
Moderately Old
563 
Undefined
307 
Old Property
303 

Length

Max length18
Median length14
Mean length13.38472
Min length9

Characters and Unicode

Total characters49229
Distinct characters25
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowRelatively New
2nd rowRelatively New
3rd rowRelatively New
4th rowRelatively New
5th rowRelatively New

Common Values

ValueCountFrequency (%)
Relatively New 1646
44.8%
New Property 593
 
16.1%
Moderately Old 563
 
15.3%
Undefined 307
 
8.3%
Old Property 303
 
8.2%
Under Construction 266
 
7.2%

Length

2024-08-26T15:58:06.695172image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-08-26T15:58:06.955033image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
new 2239
31.8%
relatively 1646
23.4%
property 896
12.7%
old 866
 
12.3%
moderately 563
 
8.0%
undefined 307
 
4.4%
under 266
 
3.8%
construction 266
 
3.8%

Most occurring characters

ValueCountFrequency (%)
e 8433
17.1%
l 4721
 
9.6%
t 3637
 
7.4%
3371
 
6.8%
y 3105
 
6.3%
r 2887
 
5.9%
d 2309
 
4.7%
N 2239
 
4.5%
w 2239
 
4.5%
i 2219
 
4.5%
Other values (15) 14069
28.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 38809
78.8%
Uppercase Letter 7049
 
14.3%
Space Separator 3371
 
6.8%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 8433
21.7%
l 4721
12.2%
t 3637
9.4%
y 3105
 
8.0%
r 2887
 
7.4%
d 2309
 
5.9%
w 2239
 
5.8%
i 2219
 
5.7%
a 2209
 
5.7%
o 1991
 
5.1%
Other values (7) 5059
13.0%
Uppercase Letter
ValueCountFrequency (%)
N 2239
31.8%
R 1646
23.4%
P 896
12.7%
O 866
 
12.3%
U 573
 
8.1%
M 563
 
8.0%
C 266
 
3.8%
Space Separator
ValueCountFrequency (%)
3371
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 45858
93.2%
Common 3371
 
6.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 8433
18.4%
l 4721
 
10.3%
t 3637
 
7.9%
y 3105
 
6.8%
r 2887
 
6.3%
d 2309
 
5.0%
N 2239
 
4.9%
w 2239
 
4.9%
i 2219
 
4.8%
a 2209
 
4.8%
Other values (14) 11860
25.9%
Common
ValueCountFrequency (%)
3371
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 49229
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 8433
17.1%
l 4721
 
9.6%
t 3637
 
7.4%
3371
 
6.8%
y 3105
 
6.3%
r 2887
 
5.9%
d 2309
 
4.7%
N 2239
 
4.5%
w 2239
 
4.5%
i 2219
 
4.5%
Other values (15) 14069
28.6%

super_built_up_area
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct593
Distinct (%)31.6%
Missing1803
Missing (%)49.0%
Infinite0
Infinite (%)0.0%
Mean1925.2376
Minimum89
Maximum10000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size57.5 KiB
2024-08-26T15:58:07.309842image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum89
5-th percentile767
Q11479.5
median1828
Q32215
95-th percentile3185
Maximum10000
Range9911
Interquartile range (IQR)735.5

Descriptive statistics

Standard deviation764.17218
Coefficient of variation (CV)0.39692356
Kurtosis10.349191
Mean1925.2376
Median Absolute Deviation (MAD)372
Skewness1.8364563
Sum3609820.5
Variance583959.12
MonotonicityNot monotonic
2024-08-26T15:58:07.655116image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1950 37
 
1.0%
1650 37
 
1.0%
2000 25
 
0.7%
1578 25
 
0.7%
2150 22
 
0.6%
1640 22
 
0.6%
1900 19
 
0.5%
2408 19
 
0.5%
1930 18
 
0.5%
2812 17
 
0.5%
Other values (583) 1634
44.4%
(Missing) 1803
49.0%
ValueCountFrequency (%)
89 1
< 0.1%
145 1
< 0.1%
161 1
< 0.1%
215 1
< 0.1%
216 1
< 0.1%
325 1
< 0.1%
340 1
< 0.1%
352 1
< 0.1%
380 1
< 0.1%
406 1
< 0.1%
ValueCountFrequency (%)
10000 1
< 0.1%
6926 1
< 0.1%
6000 1
< 0.1%
5800 2
0.1%
5514 1
< 0.1%
5350 2
0.1%
5200 2
0.1%
4890 1
< 0.1%
4857 1
< 0.1%
4848 2
0.1%

built_up_area
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED 

Distinct644
Distinct (%)38.1%
Missing1987
Missing (%)54.0%
Infinite0
Infinite (%)0.0%
Mean2379.0201
Minimum2
Maximum737147
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size57.5 KiB
2024-08-26T15:58:07.991706image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile240.5
Q11100
median1650
Q32400
95-th percentile4690
Maximum737147
Range737145
Interquartile range (IQR)1300

Descriptive statistics

Standard deviation17937.586
Coefficient of variation (CV)7.5399051
Kurtosis1668.8557
Mean2379.0201
Median Absolute Deviation (MAD)650
Skewness40.718585
Sum4022923
Variance3.2175699 × 108
MonotonicityNot monotonic
2024-08-26T15:58:08.331903image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1800 41
 
1.1%
3240 37
 
1.0%
1900 34
 
0.9%
2700 33
 
0.9%
1350 33
 
0.9%
900 28
 
0.8%
1600 26
 
0.7%
2000 24
 
0.7%
1300 24
 
0.7%
1700 23
 
0.6%
Other values (634) 1388
37.7%
(Missing) 1987
54.0%
ValueCountFrequency (%)
2 1
 
< 0.1%
14 1
 
< 0.1%
30 1
 
< 0.1%
33 1
 
< 0.1%
50 3
0.1%
53 1
 
< 0.1%
55 1
 
< 0.1%
56 1
 
< 0.1%
57 1
 
< 0.1%
60 5
0.1%
ValueCountFrequency (%)
737147 1
 
< 0.1%
13500 1
 
< 0.1%
11286 1
 
< 0.1%
9500 1
 
< 0.1%
9000 7
0.2%
8775 1
 
< 0.1%
8286 1
 
< 0.1%
8067.8 1
 
< 0.1%
8000 1
 
< 0.1%
7500 2
 
0.1%

carpet_area
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED 

Distinct733
Distinct (%)39.1%
Missing1805
Missing (%)49.1%
Infinite0
Infinite (%)0.0%
Mean2528.3204
Minimum15
Maximum607936
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size57.5 KiB
2024-08-26T15:58:08.668124image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum15
5-th percentile350
Q1845
median1300
Q31790
95-th percentile2950
Maximum607936
Range607921
Interquartile range (IQR)945

Descriptive statistics

Standard deviation22793.776
Coefficient of variation (CV)9.0153829
Kurtosis604.86175
Mean2528.3204
Median Absolute Deviation (MAD)470
Skewness24.339743
Sum4735544
Variance5.1955624 × 108
MonotonicityNot monotonic
2024-08-26T15:58:08.997949image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1400 42
 
1.1%
1800 35
 
1.0%
1600 35
 
1.0%
1200 31
 
0.8%
1500 29
 
0.8%
1650 28
 
0.8%
1350 27
 
0.7%
1300 23
 
0.6%
1000 22
 
0.6%
1450 22
 
0.6%
Other values (723) 1579
42.9%
(Missing) 1805
49.1%
ValueCountFrequency (%)
15 1
 
< 0.1%
33 1
 
< 0.1%
48 1
 
< 0.1%
50 1
 
< 0.1%
59 1
 
< 0.1%
60 1
 
< 0.1%
66 1
 
< 0.1%
72 1
 
< 0.1%
76.44 3
0.1%
77.31 1
 
< 0.1%
ValueCountFrequency (%)
607936 1
< 0.1%
569243 1
< 0.1%
514396 1
< 0.1%
64529 1
< 0.1%
64412 1
< 0.1%
58141 1
< 0.1%
54917 1
< 0.1%
48811 1
< 0.1%
45966 1
< 0.1%
34401 1
< 0.1%

study room
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size237.1 KiB
0
2973 
1
705 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters3678
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 2973
80.8%
1 705
 
19.2%

Length

2024-08-26T15:58:09.307785image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-08-26T15:58:09.559569image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 2973
80.8%
1 705
 
19.2%

Most occurring characters

ValueCountFrequency (%)
0 2973
80.8%
1 705
 
19.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 3678
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 2973
80.8%
1 705
 
19.2%

Most occurring scripts

ValueCountFrequency (%)
Common 3678
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 2973
80.8%
1 705
 
19.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3678
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 2973
80.8%
1 705
 
19.2%

servant room
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size237.1 KiB
0
2350 
1
1328 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters3678
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row0
4th row0
5th row1

Common Values

ValueCountFrequency (%)
0 2350
63.9%
1 1328
36.1%

Length

2024-08-26T15:58:09.812964image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-08-26T15:58:10.042841image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 2350
63.9%
1 1328
36.1%

Most occurring characters

ValueCountFrequency (%)
0 2350
63.9%
1 1328
36.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 3678
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 2350
63.9%
1 1328
36.1%

Most occurring scripts

ValueCountFrequency (%)
Common 3678
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 2350
63.9%
1 1328
36.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3678
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 2350
63.9%
1 1328
36.1%

store room
Categorical

IMBALANCE 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size237.1 KiB
0
3340 
1
338 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters3678
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 3340
90.8%
1 338
 
9.2%

Length

2024-08-26T15:58:10.297705image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-08-26T15:58:10.539515image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 3340
90.8%
1 338
 
9.2%

Most occurring characters

ValueCountFrequency (%)
0 3340
90.8%
1 338
 
9.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 3678
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 3340
90.8%
1 338
 
9.2%

Most occurring scripts

ValueCountFrequency (%)
Common 3678
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 3340
90.8%
1 338
 
9.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3678
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 3340
90.8%
1 338
 
9.2%

pooja room
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size237.1 KiB
0
3022 
1
656 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters3678
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 3022
82.2%
1 656
 
17.8%

Length

2024-08-26T15:58:10.792662image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-08-26T15:58:11.022539image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 3022
82.2%
1 656
 
17.8%

Most occurring characters

ValueCountFrequency (%)
0 3022
82.2%
1 656
 
17.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 3678
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 3022
82.2%
1 656
 
17.8%

Most occurring scripts

ValueCountFrequency (%)
Common 3678
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 3022
82.2%
1 656
 
17.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3678
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 3022
82.2%
1 656
 
17.8%

others
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size237.1 KiB
0
3273 
1
405 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters3678
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 3273
89.0%
1 405
 
11.0%

Length

2024-08-26T15:58:11.277403image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-08-26T15:58:11.514184image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 3273
89.0%
1 405
 
11.0%

Most occurring characters

ValueCountFrequency (%)
0 3273
89.0%
1 405
 
11.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 3678
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 3273
89.0%
1 405
 
11.0%

Most occurring scripts

ValueCountFrequency (%)
Common 3678
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 3273
89.0%
1 405
 
11.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3678
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 3273
89.0%
1 405
 
11.0%

furnishing_type
Categorical

Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size237.1 KiB
0
2421 
1
1051 
2
 
206

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters3678
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row1
5th row0

Common Values

ValueCountFrequency (%)
0 2421
65.8%
1 1051
28.6%
2 206
 
5.6%

Length

2024-08-26T15:58:11.767506image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-08-26T15:58:12.012373image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 2421
65.8%
1 1051
28.6%
2 206
 
5.6%

Most occurring characters

ValueCountFrequency (%)
0 2421
65.8%
1 1051
28.6%
2 206
 
5.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 3678
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 2421
65.8%
1 1051
28.6%
2 206
 
5.6%

Most occurring scripts

ValueCountFrequency (%)
Common 3678
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 2421
65.8%
1 1051
28.6%
2 206
 
5.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3678
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 2421
65.8%
1 1051
28.6%
2 206
 
5.6%

luxury_score
Real number (ℝ)

ZEROS 

Distinct161
Distinct (%)4.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean71.50571
Minimum0
Maximum174
Zeros462
Zeros (%)12.6%
Negative0
Negative (%)0.0%
Memory size57.5 KiB
2024-08-26T15:58:12.287192image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q131
median59
Q3110
95-th percentile174
Maximum174
Range174
Interquartile range (IQR)79

Descriptive statistics

Standard deviation53.053668
Coefficient of variation (CV)0.7419501
Kurtosis-0.87964859
Mean71.50571
Median Absolute Deviation (MAD)38
Skewness0.45943579
Sum262998
Variance2814.6917
MonotonicityNot monotonic
2024-08-26T15:58:12.619168image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 462
 
12.6%
49 348
 
9.5%
174 195
 
5.3%
44 60
 
1.6%
38 55
 
1.5%
165 55
 
1.5%
72 52
 
1.4%
60 47
 
1.3%
37 45
 
1.2%
42 45
 
1.2%
Other values (151) 2314
62.9%
ValueCountFrequency (%)
0 462
12.6%
5 6
 
0.2%
6 6
 
0.2%
7 41
 
1.1%
8 30
 
0.8%
9 9
 
0.2%
12 6
 
0.2%
13 10
 
0.3%
14 12
 
0.3%
15 43
 
1.2%
ValueCountFrequency (%)
174 195
5.3%
169 1
 
< 0.1%
168 9
 
0.2%
167 21
 
0.6%
166 10
 
0.3%
165 55
 
1.5%
161 3
 
0.1%
160 28
 
0.8%
159 23
 
0.6%
158 34
 
0.9%

Interactions

2024-08-26T15:57:49.746986image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-08-26T15:57:25.618220image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-08-26T15:57:29.160106image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-08-26T15:57:31.651578image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-08-26T15:57:33.987109image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-08-26T15:57:36.497803image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-08-26T15:57:38.993306image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-08-26T15:57:41.486048image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-08-26T15:57:44.771894image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-08-26T15:57:47.281620image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-08-26T15:57:49.987809image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-08-26T15:57:26.081892image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-08-26T15:57:29.407018image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-08-26T15:57:31.877005image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-08-26T15:57:34.229124image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-08-26T15:57:36.740921image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-08-26T15:57:39.221473image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-08-26T15:57:41.773051image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-08-26T15:57:45.033378image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-08-26T15:57:47.514872image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-08-26T15:57:50.229652image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-08-26T15:57:26.435085image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-08-26T15:57:29.652260image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-08-26T15:57:32.113709image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-08-26T15:57:34.484385image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-08-26T15:57:36.981159image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-08-26T15:57:39.457044image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-08-26T15:57:42.108602image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-08-26T15:57:45.326514image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-08-26T15:57:47.757957image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-08-26T15:57:50.451671image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-08-26T15:57:26.747754image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-08-26T15:57:29.883580image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-08-26T15:57:32.327913image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-08-26T15:57:34.717744image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-08-26T15:57:37.221779image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-08-26T15:57:39.679446image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-08-26T15:57:42.630862image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-08-26T15:57:45.560563image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-08-26T15:57:48.024130image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-08-26T15:57:50.725111image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-08-26T15:57:27.153208image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-08-26T15:57:30.151511image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-08-26T15:57:32.577912image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-08-26T15:57:34.994654image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-08-26T15:57:37.474726image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-08-26T15:57:39.933089image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-08-26T15:57:42.988508image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-08-26T15:57:45.827211image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-08-26T15:57:48.275946image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-08-26T15:57:51.003609image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-08-26T15:57:27.508367image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-08-26T15:57:30.416897image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-08-26T15:57:32.821517image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-08-26T15:57:35.250343image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-08-26T15:57:37.742538image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-08-26T15:57:40.180947image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-08-26T15:57:43.315031image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-08-26T15:57:46.103624image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-08-26T15:57:48.530872image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-08-26T15:57:51.242702image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-08-26T15:57:27.936739image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-08-26T15:57:30.658771image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-08-26T15:57:33.040460image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-08-26T15:57:35.482921image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-08-26T15:57:37.989472image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-08-26T15:57:40.394875image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-08-26T15:57:43.596327image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-08-26T15:57:46.344611image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-08-26T15:57:48.755267image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-08-26T15:57:51.476291image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-08-26T15:57:28.235857image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-08-26T15:57:30.896728image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-08-26T15:57:33.265186image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-08-26T15:57:35.733874image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-08-26T15:57:38.215380image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-08-26T15:57:40.628099image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-08-26T15:57:43.913125image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-08-26T15:57:46.555316image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-08-26T15:57:49.046216image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-08-26T15:57:51.737776image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-08-26T15:57:28.532811image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-08-26T15:57:31.156528image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-08-26T15:57:33.511046image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-08-26T15:57:36.000418image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-08-26T15:57:38.478713image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-08-26T15:57:40.878047image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-08-26T15:57:44.165886image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-08-26T15:57:46.804401image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-08-26T15:57:49.261942image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-08-26T15:57:51.979756image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-08-26T15:57:28.882586image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-08-26T15:57:31.397943image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-08-26T15:57:33.748790image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-08-26T15:57:36.243464image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-08-26T15:57:38.724633image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-08-26T15:57:41.156679image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-08-26T15:57:44.474555image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-08-26T15:57:47.027346image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-08-26T15:57:49.497168image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-08-26T15:58:13.143886image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
agePossessionareabalconybathroombedRoombuilt_up_areacarpet_areafacingfloorNumfurnishing_typeluxury_scoreotherspooja roompriceprice_per_sqftproperty_typeservant roomstore roomstudy roomsuper_built_up_area
agePossession1.0000.0000.2730.1110.1300.0000.0000.0920.1260.2130.2550.1080.1870.1020.0560.3790.2870.1430.1410.086
area0.0001.0000.0110.6870.6240.8350.8010.0220.1160.0430.2590.0420.0370.7440.2070.0280.0150.0390.0180.948
balcony0.2730.0111.0000.2260.1760.0000.0260.0170.0790.1770.2230.0820.1970.1360.0330.2140.4410.1460.1830.306
bathroom0.1110.6870.2261.0000.8620.4650.5990.044-0.0040.1980.1790.0700.2860.7200.4110.4720.5200.2440.1760.819
bedRoom0.1300.6240.1760.8621.0000.3800.5680.032-0.1040.1680.0570.0790.2910.6810.4170.5950.3170.2230.1540.800
built_up_area0.0000.8350.0000.4650.3801.0000.9691.0000.0920.0880.2890.0000.0000.6050.1320.0000.0000.0000.0000.926
carpet_area0.0000.8010.0260.5990.5680.9691.0000.0000.1590.0000.2390.0160.0000.6130.1360.0000.0000.0000.0030.894
facing0.0920.0220.0170.0440.0321.0000.0001.0000.0000.0490.0650.0000.0300.0210.0000.0940.0370.0350.0000.000
floorNum0.1260.1160.079-0.004-0.1040.0920.1590.0001.0000.0160.2320.0330.1020.001-0.1260.4840.0840.1120.0780.152
furnishing_type0.2130.0430.1770.1980.1680.0880.0000.0490.0161.0000.2420.0600.2160.1760.0230.0830.2680.1530.1410.132
luxury_score0.2550.2590.2230.1790.0570.2890.2390.0650.2320.2421.0000.1760.1890.2150.0540.3290.3470.2290.1830.222
others0.1080.0420.0820.0700.0790.0000.0160.0000.0330.0600.1761.0000.0330.0340.0360.0260.0000.1060.0310.084
pooja room0.1870.0370.1970.2860.2910.0000.0000.0300.1020.2160.1890.0331.0000.3340.0430.2520.2520.3050.3130.157
price0.1020.7440.1360.7200.6810.6050.6130.0210.0010.1760.2150.0340.3341.0000.7440.5430.3690.3030.2440.772
price_per_sqft0.0560.2070.0330.4110.4170.1320.1360.000-0.1260.0230.0540.0360.0430.7441.0000.2010.0440.0000.0300.287
property_type0.3790.0280.2140.4720.5950.0000.0000.0940.4840.0830.3290.0260.2520.5430.2011.0000.0650.2410.1281.000
servant room0.2870.0150.4410.5200.3170.0000.0000.0370.0840.2680.3470.0000.2520.3690.0440.0651.0000.1610.1850.584
store room0.1430.0390.1460.2440.2230.0000.0000.0350.1120.1530.2290.1060.3050.3030.0000.2410.1611.0000.2260.046
study room0.1410.0180.1830.1760.1540.0000.0030.0000.0780.1410.1830.0310.3130.2440.0300.1280.1850.2261.0000.121
super_built_up_area0.0860.9480.3060.8190.8000.9260.8940.0000.1520.1320.2220.0840.1570.7720.2871.0000.5840.0460.1211.000

Missing values

2024-08-26T15:57:52.391677image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-08-26T15:57:53.119446image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2024-08-26T15:57:53.652761image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

property_typesocietysectorpriceprice_per_sqftareaareaWithTypebedRoombathroombalconyfloorNumfacingagePossessionsuper_built_up_areabuilt_up_areacarpet_areastudy roomservant roomstore roompooja roomothersfurnishing_typeluxury_score
0flatvatika gurgaonsector 831.256313.01980.0Super Built up area 1980(183.95 sq.m.)3322.0WestRelatively New1980.0NaNNaN010000119
1flatparas dewssector 1061.176647.01760.0Super Built up area 1760(163.51 sq.m.)Built Up area: 1186 sq.ft. (110.18 sq.m.)Carpet area: 1130 sq.ft. (104.98 sq.m.)343+12.0WestRelatively New1760.01186.01130.001000049
2flatshapoorji pallonji joyville gurugramsector 1022.4010545.02276.0Super Built up area 2162(200.86 sq.m.)443+15.0North-EastRelatively New2162.0NaNNaN000000108
3flattulip violetsector 691.658872.01860.0Built Up area: 1578 (146.6 sq.m.)3321.0NaNRelatively NewNaN1578.0NaN00000169
4flatsatya the hermitagesector 1030.896137.01450.0Super Built up area 1450(134.71 sq.m.)Carpet area: 1081 sq.ft. (100.43 sq.m.)2331.0NaNRelatively New1450.0NaN1081.001000075
5flatpioneer parksector 613.6012627.02851.0Super Built up area 2851(264.87 sq.m.)4433.0NorthModerately Old2851.0NaNNaN010001121
6flatdeepak mindamanesar1.254716.02651.0Carpet area: 2650 (246.19 sq.m.)333+6.0NaNModerately OldNaNNaN2650.001000044
7flataipl the peaceful homessector 70a2.5010638.02350.0Super Built up area 2350(218.32 sq.m.)3438.0SouthRelatively New2350.0NaNNaN011001174
8flatashiana apartmentsector 230.162051.0780.0Super Built up area 780(72.46 sq.m.)2221.0NaNModerately Old780.0NaNNaN0000000
9houseashok vihar phase iii extensionsector 3 phase 3 extension0.408889.0450.0Plot area 50(41.81 sq.m.)743+4.0NaNNew PropertyNaN450.0NaN0001007
property_typesocietysectorpriceprice_per_sqftareaareaWithTypebedRoombathroombalconyfloorNumfacingagePossessionsuper_built_up_areabuilt_up_areacarpet_areastudy roomservant roomstore roompooja roomothersfurnishing_typeluxury_score
3793flatexperion the heartsongsector 1081.308344.01558.0Super Built up area 1758(163.32 sq.m.)Carpet area: 1558 sq.ft. (144.74 sq.m.)333+7.0South-WestRelatively New1758.0NaN1558.0100000150
3794flatparas dewssector 1062.108917.02355.0Super Built up area 2355(218.79 sq.m.)463+6.0South-EastRelatively New2355.0NaNNaN01001060
3795flatramsons kshitijsector 950.17544.03125.0Carpet area: 3212 (298.4 sq.m.)11112.0North-EastRelatively NewNaNNaN3212.000000056
3796flatsssector 852.147610.02812.0Super Built up area 2812(261.24 sq.m.)Built Up area: 2300 sq.ft. (213.68 sq.m.)Carpet area: 2000 sq.ft. (185.81 sq.m.)4436.0NorthRelatively New2812.02300.02000.011010049
3797flatmariners homesector 562.118612.02450.0Super Built up area 2450(227.61 sq.m.)Built Up area: 2250 sq.ft. (209.03 sq.m.)Carpet area: 2150 sq.ft. (199.74 sq.m.)3234.0SouthRelatively New2450.02250.02150.0000011126
3798flatthe close northsector 502.3511767.01997.0Super Built up area 1997(185.53 sq.m.)Built Up area: 1950 sq.ft. (181.16 sq.m.)Carpet area: 1850 sq.ft. (171.87 sq.m.)333+8.0North-EastModerately Old1997.01950.01850.0000001165
3799flatcghs hewo apartmentssector 311.2511574.01080.0Carpet area: 1080 (100.34 sq.m.)2222.0NaNOld PropertyNaNNaN1080.000000028
3800flatshree vardhman florasector 900.654961.01310.0Super Built up area 1300(120.77 sq.m.)Built Up area: 1225 sq.ft. (113.81 sq.m.)Carpet area: 1075 sq.ft. (99.87 sq.m.)22311.0North-WestRelatively New1300.01225.01075.010000149
3801flatsignature global the millenniasector 37d0.6510906.0596.0Super Built up area 650(60.39 sq.m.)Carpet area: 596 sq.ft. (55.37 sq.m.)2230.0NaNNew Property650.0NaN596.000000040
3802flattarc maceosector 911.807758.02320.0Carpet area: 2320 (215.54 sq.m.)433+0.0NorthRelatively NewNaNNaN2320.010010245